Evidence map›Paper›PMID 35171108›Full record

ArticleJMIR human factors2022

Patient Portal Messaging for Asynchronous Virtual Care During the COVID-19 Pandemic: Retrospective Analysis.

Ming Huang, Aditya Khurana, George Mastorakos, Andrew Wen, Huan He, Liwei Wang, Sijia Liu, Yanshan Wang, Nansu Zong, Julie Prigge and 6 more

Abstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Telehealth Trends and Hypertension Management Among Rural and Medicaid Patients After COVID-19.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2024
    Article
  11. Article
  12. Patient Portal Use during Home Health Care at an Academic Health System.Journal of the American Medical Directors Association · 2024
    Article
  13. A scoping review of empathy recognition in text using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  14. Article
  15. Use of the patient portal among older adults with diagnosed dementia and their care partners.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
    Observational
  16. Article
  17. Review
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Ming HuangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0001-7367-3626
Aditya KhuranaMayo Clinic Alix School of Medicine, Mayo Clinic, Scottsdale, AZ, United States.ORCID https://orcid.org/0000-0001-9148-9531
George MastorakosMayo Clinic Alix School of Medicine, Mayo Clinic, Scottsdale, AZ, United States.ORCID https://orcid.org/0000-0002-6155-6788
Andrew WenDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0001-9090-8028
Huan HeDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0003-1312-4195
Liwei WangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0001-9970-8604
Sijia LiuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0001-9763-1164
Yanshan WangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0003-4433-7839
Nansu ZongDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0003-0066-9524
Julie PriggeCenter for Connected Care, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0002-4709-4142
Brian CostelloCenter for Connected Care, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0003-1753-5928
Nilay ShahDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0002-8597-3447
Henry TingDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0002-1495-584X
Jungwei FanDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0001-6349-3752
Christi PattenCenter for Clinical and Translational Science, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0002-7194-8160
Hongfang LiuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0003-2570-3741

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI AKHILESH PANDEY · 1985 to 2026
$151.3M
Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
NCATS NIH HHS U01 TR002062NCI NIH HHS P30 CA015083
6 · The paper itself

Abstract

backgroundDuring the COVID-19 pandemic, patient portals and their message platforms allowed remote access to health care. Utilization patterns in patient messaging during the COVID-19 crisis have not been studied thoroughly. In this work, we propose characterizing patients and their use of asynchronous virtual care for COVID-19 via a retrospective analysis of patient portal messages.

objectiveThis study aimed to perform a retrospective analysis of portal messages to probe asynchronous patient responses to the COVID-19 crisis.

methodsWe collected over 2 million patient-generated messages (PGMs) at Mayo Clinic during February 1 to August 31, 2020. We analyzed descriptive statistics on PGMs related to COVID-19 and incorporated patients' sociodemographic factors into the analysis. We analyzed the PGMs on COVID-19 in terms of COVID-19-related care (eg, COVID-19 symptom self-assessment and COVID-19 tests and results) and other health issues (eg, appointment cancellation, anxiety, and depression).

resultsThe majority of PGMs on COVID-19 pertained to COVID-19 symptom self-assessment (42.50%) and COVID-19 tests and results (30.84%). The PGMs related to COVID-19 symptom self-assessment and COVID-19 test results had dynamic patterns and peaks similar to the newly confirmed cases in the United States and in Minnesota. The trend of PGMs related to COVID-19 care plans paralleled trends in newly hospitalized cases and deaths. After an initial peak in March, the PGMs on issues such as appointment cancellations and anxiety regarding COVID-19 displayed a declining trend. The majority of message senders were 30-64 years old, married, female, White, or urban residents. This majority was an even higher proportion among patients who sent portal messages on COVID-19.

conclusionsDuring the COVID-19 pandemic, patients increased portal messaging utilization to address health care issues about COVID-19 (in particular, symptom self-assessment and tests and results). Trends in message usage closely followed national trends in new cases and hospitalizations. There is a wide disparity for minority and rural populations in the use of PGMs for addressing the COVID-19 crisis.

Indexed as

asynchronous communicationCOVID-19digital healthhealth carehealthcarepandemicpatient portalpatient portal messageremote healthcareutilizationvirtual care

Identifiers

PMID35171108
PMCPMC9084445

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.